Abstract
In a customer service call center, when an issue is reported by a customer, a SR (service request) is logged. Often, additional logs are needed to resolve the SR; typically, an engineer connects with the customer over a live maintenance window (MW) to address the issue. Solving an issue can also be quite long or can involve scheduling multiple MWs before a root cause is identified. Thus, issues with maintenance windows, scheduling such windows, and potentially having multiple customer interactions to fully address an issue can impact a company's ability to provide quality service to the customer. Techniques to address the general ineffectiveness of customer maintenance calls are provided herein. Specifically, technique proposed herein provide for automating the live debugging procedure using real-time data processing, where relevant data can be collected and parsed from an end device through a request-response mechanism, and Retrieval Augmented Generation (RAG) and generative artificial intelligence (GenAI) models can be leveraged to provide meaningful insights into an issue, which may provide for the ability to quickly resolve customer issues.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
B, Nivetha and Rathod, Santosh, "AUTOMATED LIVE DEBUGGING USING REAL-TIME DATA PROCESSING", Technical Disclosure Commons, (February 27, 2025)
https://www.tdcommons.org/dpubs_series/7856